Sources of Bias in Clinical Artificial Intelligence and Applications in Rheumatology.

Rheumatology machine-learning models are limited by preexisting, technical, and emergent biases; the interaction of data constraints, design choices, and real-world clinical workflows, rather than from isolated technical errors. Across the model lifecycle, optimization objectives can encode patterns of care, access, and documentation, producing hidden subgroup failures that are obscured by aggregate performance metrics. Given the heterogeneity of rheumatic disease and current disparities in care delivery, addressing bias requires deliberate design choices before, during, and after a model is built, as well as a commitment to transparency, and sustained oversight.